Method and system for determining potential for energy usage improvements in built environment

ABSTRACT

The present disclosure provides a method and system for calculation of a probabilistic score. The probabilistic score is used to determine a potential for improvements in energy consumption inside a built environment. The method includes a step of collecting a first set of statistical data. The method includes yet another step of receiving a second set of statistical data. The second set of statistical data is associated with each of a plurality of users present inside the built environment. The method includes yet another step of comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison is performed to determine the potential for improvement in the energy consumption of each of the plurality of energy consuming devices. The method includes yet another step of calculating the probabilistic score.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Ser. No. 62/181,351, filed Jun. 18, 2015 and entitled “SYSTEM AND METHOD FOR A DISTRIBUTED APPROACH TO DATA COLLECTION AND DISPLAY TO DEVELOP A PERSISTENT ENERGY MODEL WHICH INCLUDES MOVABLE AND FIXED ENERGY CONSUMING LOAD SOURCES AND ESTABLISHES A REFERENCE OF DISAGGREGATED ENERGY CONSUMPTION OF SPECIFIC APPLIANCES AND LOADS IN A BUILT ENVIRONMENT”, the disclosure of which is herein incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a field of energy management systems. More specifically, the present disclosure relates to an energy auditing and scoring system for energy usage improvement in a built environment.

BACKGROUND

With the proliferation of growth of technological advancements in the built environments, energy consumption has reached new soaring heights. Moreover, the energy consumption has increased due to greater use of existing systems due to poor maintenance or greater utilization of various electrical and mechanical systems installed in the built environments which consistently draw energy for performing various tasks. In addition, the energy consumption is increased through various fixed energy consuming devices installed in the built environment and portable communication devices associated with various users living in the built environment or regularly visiting the built environment for various purposes. With the growth of this infrastructure as well as the increase in the energy consuming devices that are associated with the infrastructure, a need to study and derive the energy efficiency is required to progressively analyze and provide energy efficient solutions to the built environment.

Further, the energy efficiency of commercial as well as non-commercial built environments has gathered a lot of attention in recent years. In many areas of the world, the commercial built environments and the non-commercial built environments consume a good portion of the generated electricity available on an electric grid. This is putting an increasing amount of burden on the electrical grid due to which demand of electrical power keeps rising day by day. Moreover, a higher demand of electrical energy by the infrastructure associated with the built environments requires a scaled operation and necessitates for a cost effective solution. The need for keeping a consistent check on the energy consumption of the built environment has increased gradually.

Furthermore, an increase in demand of performance assessment of the built environment has generated a plurality of measures. Many energy auditing techniques are employed these days for determining the energy consumption of the built environment. One of the measures of assessing the energy efficiency is calculation of an Energy Star score. The Energy Star score measures the energy efficiency of the built environment on a scale ranging from 1 to 100 indicating a relative performance between similar built environments. Moreover, the energy consumption of a building is recorded based on a current consumption. Also, each building has a different pattern of energy consumption based on the type or end use of the built environment.

Going further, the existing systems and methods for determining the energy consumption of the built environment are inefficient. Moreover, depending on the available data sources the system for determining the Energy Star score or other standard practices to asses building energy performance can use many spreadsheets or comma separated to record a plurality of statistical data pertaining to the energy consumption of the built environment. The spreadsheets are later fed to computer software to perform analysis. The system takes a lot of time and a lot of human resource is utilized in the recording of the statistical data which increases cost of the assessment and determination of Energy Star score or other building performance metrics. In addition, the present systems and methods do not take into account the past energy consumption of the built environment with the consideration of the occupancy pattern of various users inside the built environment. Further, the present systems and methods do not determine whether the built environment has a potential for improvement in the energy consumption or not. Moreover, a lack of proper data visualization systems without live monitoring generates a lot of loopholes in understanding of the energy efficiency of the built environment.

In light of the above stated discussion, there is a need for a method and system that overcomes the above stated disadvantages.

SUMMARY

In one aspect, the present disclosure provides a method for calculation of a probabilistic score. The probabilistic score is used to determine a potential for improvements in energy consumption inside a built environment. The method includes a step of collecting a first set of statistical data. The first set of statistical data is associated with a plurality of energy consuming devices present in the built environment. The method includes yet another step of receiving a second set of statistical data. The second set of statistical data is associated with each of a plurality of users present inside the built environment. The method includes yet another step of comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison is performed to determine the potential for improvement in the energy consumption of each of the plurality of energy consuming devices. The method includes yet another step of calculating the probabilistic score. The probabilistic score is calculated to determine the potential for improvements in the energy consumption of the built environment. The first set of statistical data includes a current energy consumption data and a past energy consumption data. The current energy consumption data and the past energy consumption data is associated with each of the plurality of energy consuming devices. The first set of statistical data is collected based on a first plurality of parameters and the first set of statistical data is collected in real time. The second set of statistical data is received based on a second plurality of parameters. The second set of statistical data is received in the real time. The current energy consumption data and the past energy consumption data are compared in the real time. The probabilistic score is calculated to provide a rating to each of the plurality of energy consuming devices. The calculation of the probabilistic score is performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

In an embodiment of the present disclosure, the x-y intercept denotes the energy consumption of each of the plurality of the energy consuming devices.

In another embodiment of the present disclosure, the probabilistic score is improved by an application of a learning algorithm. The application of learning algorithm includes recording of the first set of statistical data and an operating behavior. The operating behavior is associated with the plurality of users. The operating behavior is recorded based on a type of the built environment, a physical location and duration of energy usage for each of plurality of portable communication devices.

In an embodiment of the present disclosure, the method includes yet another step of segregating the first set of statistical data. The first set of statistical data is segregated by creating one or more fields. The one or more fields pertain to the energy consumption of each of the plurality of energy consuming devices. The one or more fields are created based on one or more operating characteristics and one or more physical characteristics of the plurality of energy consuming devices. The one or more operating characteristics include an operating voltage, running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate. The one or more physical characteristics include size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.

In an embodiment of the present disclosure, the plurality of sources of the past energy consumption data and weather conditions includes a plurality of external application programming interfaces and third party databases.

In an embodiment of the present disclosure, the plurality of energy consuming devices includes a plurality of electrical devices and a plurality of portable communication devices. The first set of statistical data is collected manually and electronically.

In an embodiment of the present disclosure, the first plurality of parameters includes the current energy consumption data, a physical location, duration of energy usage, a seasonal variation and an off-seasonal variation in the energy consumption.

In an embodiment of the present disclosure, the second plurality of parameters includes an occupancy behavior of the plurality of users, an energy consuming pattern, the physical location of the each of the plurality of users. In addition, the second plurality of parameters comprises the duration of the energy usage of energy consuming devices associated with each of the plurality of users. In addition, the energy consuming pattern corresponds to each energy consuming device associated with each of the plurality of users.

In an embodiment of the present disclosure, the method includes yet another step of storing the first set of statistical data, the second set of statistical data, and the probabilistic score. The first set of statistical data, the second set of statistical data and the probabilistic score are stored in the real time.

In an embodiment of the present disclosure, the method includes yet another step of updating the first set of statistical data, the second set of statistical data and the probabilistic score.

In another aspect, the present disclosure provides a computer system. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processors. The memory is used to store instructions. The instructions in the memory when executed by the one or more processors cause the one or more processors to perform a method. The one or more processors perform the method for calculation of a probabilistic score. The probabilistic score is used to determine a potential for improvements in energy consumption inside a built environment. The method includes a step of collecting a first set of statistical data. The first set of statistical data is associated with a plurality of energy consuming devices present in the built environment. The method includes yet another step of receiving a second set of statistical data. The second set of statistical data is associated with each of a plurality of users present inside the built environment. The method includes yet another step of comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison is performed to determine the potential for improvement in the energy consumption of each of the plurality of energy consuming devices. The method includes yet another step of calculating the probabilistic score. The probabilistic score is calculated to determine the potential for improvements in the energy consumption of the built environment. The first set of statistical data includes a current energy consumption data and a past energy consumption data. The current energy consumption data and the past energy consumption data is associated with each of the plurality of energy consuming devices. The first set of statistical data is collected based on a first plurality of parameters and the first set of statistical data is collected in real time. The second set of statistical data is received based on a second plurality of parameters. The second set of statistical data is received in the real time. The current energy consumption data and the past energy consumption data are compared in the real time. The probabilistic score is calculated to provide a rating to each of the plurality of energy consuming devices. The calculation of the probabilistic score is performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

In yet another aspect, the present disclosure provides a computer-readable storage medium. The computer readable storage medium enables encoding of computer executable instructions. The computer executable instructions when executed by at least one processor perform a method. The at least one processor performs the method for calculation of a probabilistic score. The probabilistic score is used to determine a potential for improvements in energy consumption inside a built environment. The method includes a step of collecting a first set of statistical data. The first set of statistical data is associated with a plurality of energy consuming devices present in the built environment. The method includes yet another step of receiving a second set of statistical data. The second set of statistical data is associated with each of a plurality of users present inside the built environment. The method includes yet another step of comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison is performed to determine the potential for improvement in the energy consumption of each of the plurality of energy consuming devices. The method includes yet another step of calculating the probabilistic score. The probabilistic score is calculated to determine the potential for improvements in the energy consumption of the built environment. The first set of statistical data includes a current energy consumption data and a past energy consumption data. The current energy consumption data and the past energy consumption data is associated with each of the plurality of energy consuming devices. The first set of statistical data is collected based on a first plurality of parameters and the first set of statistical data is collected in real time. The second set of statistical data is received based on a second plurality of parameters. The second set of statistical data is received in the real time. The current energy consumption data and the past energy consumption data are compared in the real time. The probabilistic score is calculated to provide a rating to each of the plurality of energy consuming devices. The calculation of the probabilistic score is performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

BRIEF DESCRIPTION OF THE FIGURES

Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates an interactive environment for determination of a potential for energy usage improvements in a built environment, in accordance with various embodiments of the present disclosure;

FIG. 2 illustrates a block diagram to calculate a probabilistic score, in accordance with various embodiments of the present disclosure;

FIG. 3 illustrates a block diagram of energy auditing and scoring system, in accordance with various embodiments of the present disclosure;

FIG. 4 illustrates a flow chart for the calculation of the probabilistic score, in accordance with various embodiments of the present disclosure; and

FIG. 5 illustrates a block diagram of a communication device, in accordance with various embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present technology. It will be apparent, however, to one skilled in the art that the present technology can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form only in order to avoid obscuring the present technology.

Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not other embodiments.

Moreover, although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to said details are within the scope of the present technology. Similarly, although many of the features of the present technology are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the present technology is set forth without any loss of generality to, and without imposing limitations upon, the present technology.

FIG. 1 illustrates an interactive environment for determination of a potential for improvement in energy usage improvements in a built environment 102, in accordance with various embodiments of the present disclosure. The potential for energy usage improvements is done based on calculation of a probabilistic score for the built environment 102. The calculation of the probabilistic score is performed to mathematically rate output of a statistical analysis of a current energy consumption data. In addition, the statistical analysis of the current energy consumption data is compared against a past energy consumption data.

Further, the interactive environment is characterized by interactions of the built environment 102, a communication network 112, an energy auditing and scoring system 114, a plurality of external application program interfaces 116 and a statistics monitoring device 118. In addition, the built environment includes a plurality of portable communication devices 108, a plurality of electrical devices 106 and one or more data collecting devices 110. The above arrangement enables the calculation of the probabilistic score. The probabilistic score is calculated based on the energy consumption data disaggregated within the built environment 102.

Furthermore, the built environment 102 utilizes the energy for operation and maintenance of the built environment 102. The built environment may obtain energy for operation and maintenance from at least one of a plurality of energy sources. The plurality of energy sources includes but may not be limited to solar energy, thermal energy, energy from gases and energy from water. In addition, each of the plurality of energy sources may be utilized to produce electrical energy. The operation and maintenance of the built environment 102 is based on types of services provided by the built environment 102. The types of services include but may not be limited to hospitality, travel, work and entertainment. In addition, the type of services provided by the built environment 102 decides scale of the operations and maintenance of the built environment 102. Examples of the built environment 102 include but may not be limited to an office, a mall, an airport, a stadium and a manufacturing plant.

The built environment 102 is associated with a plurality of users 104. The plurality of users 104 is present inside the built environment 102. The plurality of users 104 includes one or more human operators, one or more human worker, one or more occupants, one or more data managers, one or more visitors and the like. In an example, the one or more human operators monitor and regulate machines. In another example, the one or more human workers clean, sweep and repair the plurality of energy consuming devices. In yet another example, the one or more occupants include managers, attendants, assistants, clerk, security staff and the like. In yet another example, the visitors are civilians present for a specific period of time.

Each user of the plurality of users 104 utilizes a pre-defined amount of the energy. The pre-defined amount of the energy pertains to an energy consuming device of the plurality of energy consuming devices. In addition, each of the plurality of users 104 is associated with the energy consumption of the plurality of electrical devices 106. The plurality of electrical devices 106 is present inside the built environment 102. The plurality of electrical devices 106 include but may not be limited to electrical, electromechanical, fixed, portable and variable devices. In addition, the plurality of electrical devices 106 may be related or unrelated to structure and operations of the built environment 102.

The energy consumption is based on a power rating and duration of usage of each of the plurality of the electrical devices 106. The plurality of electrical devices 106 includes air conditioners, de-humidifiers, escalators, elevators, lightning devices and the like. The energy consumption is determined by evaluation, storage and analysis of a plurality of factors associated with each of the plurality of electrical devices 106. The plurality of factors includes current, voltage, power, thermal loss, device size and load rating for each of the plurality of electrical devices 106.

In addition, each of the plurality of users 104 is associated with a portable communication device of the plurality of portable communication devices 108. Each of the plurality of portable communication devices 108 consumes a pre-defined portion of the energy. The pre-defined portion of the energy is based on the duration of the energy consumption of each of the plurality of portable communication devices 108. The pre-defined portion of the energy is based on the power rating of each of the plurality of portable communication devices 108. Examples of the plurality of portable communication devices 108 includes but may not be limited to laptop, mobile phone, PDA and tablet. In addition, the pre-defined portion of the energy consumption by the each of the plurality of portable communication devices 108 is derived through the occupancy patterns of the plurality of users 104.

The one or more data collecting devices 110 are associated with the communication network 112 through an internet connection. Each of the one or more data collecting devices 110 enables digital acquisition of sensor data and physically collected data. Further, one or more users of the plurality of users 104 may be assigned to operate at least one of one or more data collecting devices 110. The daily usage and operating characteristics of each of the plurality of electrical devices 106 and each of the plurality of portable communication devices 108 are derived from interview results of assigned one or more users. Each user of the one or more users maintains records. The records pertain to a pre-defined set of questions and answers associated with the one or more operating characteristics and the one or more physical characteristics. Each of the one or more data collecting devices 110 may be a mobile device. The mobile device is associated with a user that is responsible for collecting a portion of a first set of statistical data and a second set of statistical data. The mobile device is enabled with a camera, a keypad or keyboard, a global positioning system (GPS), text and data entry application and the like. The user assigned to collect the first set of statistical data and the second set of statistical data may use the mobile device to capture images and nameplate information of each of the plurality of energy consuming devices. In addition, the user may use the mobile device to schedule, record, identify, transfer or tag information related to the identity, usage and location of the plurality of energy consuming devices. The user may tag the images or any records with metadata for current and future analysis of the first set of statistical data and the second set of statistical data.

In addition, each of the one or more data collecting devices 110 transfers the sensor data and the physically collected data to the energy auditing and scoring system 114 through the communication network 112. Each of the one or more data collecting devices 110 enables connection to a data channel for transmission of the received data from physical sources and sensors. The internet connection is established based on a type of network. In an embodiment of the present disclosure, the type of network is a wireless mobile network. In another embodiment of the present disclosure, the type of network is a wired network with a finite bandwidth. In yet another embodiment of the present disclosure, the type of network is a combination of the wireless and the wired network for an optimum throughput of data transmission. Further, the communication network 112 includes a set of channels. Each channel of the set of channels supports a finite bandwidth. The finite bandwidth of each channel of the set of channels is based on capacity of network. The communication network 112 transmits the pre-defined size of sensor data and physically collected data at the pre-defined rate to the energy auditing and scoring system 114.

Further, the energy auditing and scoring system 114 collects a first set of statistical data associated with a plurality of energy consuming devices in the built environment 102. The first set of statistical data comprises a current energy consumption data and a past energy consumption data associated with the each of the plurality of energy consuming devices. The first set of statistical data is collected based on a first plurality of parameters. The first set of statistical data may be collected through any method. In an embodiment of the present disclosure, the first set of statistical data may be collected digitally through the plurality of sensors 202 b for each of the plurality of electrical devices 106. In addition, the first set of statistical data may be collected digitally through the plurality of sensors 202 b for the plurality of portable communication devices 108. In another embodiment of the present disclosure, the first set of statistical data may be physically collected by the one or more users through mobile devices. In addition, the energy auditing and scoring system 114 segregates the first set of statistical data to create one or more fields. In addition, the one or more fields pertains to each of the plurality of energy consuming devices The one or more fields is created based on one or more operating characteristics and one or more physical characteristics. The one or more operating characteristics include an operating voltage, running load amperage, a full load amperage, wattage, voltage, frequency, an operating temperature, flow rates and the like. The one or more physical characteristics include size, dimensions, packaging, shape and the like.

In addition, the energy auditing and scoring system 114 receives another part of the first set of statistical data from the plurality of external APIs 116 and third party databases. The plurality of external APIs 116 and the third party databases collect, store and transmit weather history data, weather forecasts data, energy usage data and billing data and the like. In addition, the plurality of external APIs 116 and the third party databases collect, store and transmit the past energy consumption data, metered energy data, financial and non-financial business data.

The financial and non-financial business data comes from business management software. In an example, the business management software is Enterprise Resources Planning (ERP) software. In addition, the energy auditing and scoring system 114 requests the plurality of external APIs 116 with timed data polls. The energy auditing and scoring system 114 requests the plurality of external APIs 116 to pull and push part of the first set of statistical data. The timed data polls are programmed based on batch calendar schedule, manual requests and the real-time data feeds from on-site monitoring equipment.

In addition, the energy auditing and scoring system 114 receives a second set of statistical data from the interview results of each of the plurality of users 104. The energy auditing and scoring system 114 utilizes a statistical collection model to assimilate and parse the second set of statistical data. The statistical collection model joins information from the first set of statistical data and the second set of statistical data. The statistical collection model ascertains a probability of accuracy for comparison of different answers to the pre-defined set of questions. In addition, the statistical collection model derives an energy usage pattern from the first set of statistical data. In an example, a photocopy machine includes a copy counter to show a number of times of reproduction of image. In another example a pump motor includes a revolution counter to display operating life of the air conditioner in the building.

Further, the energy auditing and scoring system 114 stores a segregated form of the first set of statistical data and the second set of statistical data. The segregated first set of statistical data pertains to the energy consumption of each of the plurality of energy consuming devices. The collection of the first set of statistical data and the second set of statistical data is performed through a distributed audit process. The distributed audit process enables collection and analysis of the first set of statistical data and the second set of statistical data corresponding each of the plurality of energy consuming devices and each of the plurality of users. In addition, the distributed audit process is applicable for data collection across multiple platforms. The distributed audit process when divided up across a distributed digital platform enables cost effective and relatively faster means of collection of the first set of statistical data and the second set of statistical data.

The energy auditing and scoring system 114 performs a real time comparison between the current energy consumption data and the past energy consumption data of the first set of statistical data. The comparison is performed for determination of the potential for the improvements in the energy consumption. Further, the energy auditing and scoring system computes the probabilistic score for determination of the potential for improvements in the energy consumption of the built environment 102. The probabilistic score is calculated to provide a rating to each of the plurality of energy consuming devices present in the built environment 102. The calculation of the probabilistic score is performed from derivation of a relationship formula. The relationship formula pertains to finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment 102.

Further, the probabilistic score presents the comparison of the past energy consumption data and the current energy consumption data. The probabilistic score is improved by an application of a learning algorithm. The application of learning algorithm includes a record of the first set of statistical data for each of the plurality of energy consuming devices and an operating behavior of the plurality of users. The operating behavior is recorded based on a type of the built environment 102, a physical location and duration of energy usage for each of plurality of portable communication devices 108 associated with the plurality of users 104.

In addition, the energy auditing and scoring system 114 stores the first set of statistical data, the second set of statistical data and the probabilistic score. Also, the energy auditing and scoring system 114 dynamically updates the probabilistic score in the real time.

Further, the energy auditing and scoring system 114 displays the first set of statistical data, the second set of statistical data and the probabilistic score. The energy auditing and scoring system 114 displays one or more graphs, one or more tables and one or more charts to the statistics monitoring device 118. In addition, the one or more graphs, the one or more charts and the one or more tables pertain to the energy consumption, cost, finances, loss, efficiency and the like. The statistics monitoring device 118 receives and displays the probabilistic score, the first set of statistical data and the second set of statistical data. The statistics monitoring device 118 is any fixed or portable device connected to the energy auditing and scoring system 114 through the internet. Examples of the statistics monitoring device 118 include but may not be limited to mobile phone, PDA, laptop and PC. Furthermore, the statistics monitoring device 118 is associated with a user 118 a. The user 118 a monitors and evaluates errors and trends in the first set of statistical data, the second set of statistical data and the probabilistic score. The user 118 a is designated to inform authorities about changes required in the energy consumption. In addition, the user 118 a adds the one or more fields for the improvement in the first set of statistical data, the second set of statistical data and the probabilistic score.

It may be noted that in FIG. 1, the built environment 102 is connected to the energy auditing and scoring system 114; however, those skilled in the art would appreciate that more number of built environments are connected to energy auditing and scoring system 114. It may be noted that in FIG. 1, the energy auditing and scoring system 114 displays the first set of statistical data, the second set of statistical data and the probabilistic score on the statistics monitoring device 118; however, those skilled in the art would appreciate that the energy auditing and scoring system 114 displays the first set of statistical data, the second set of statistical data and the probabilistic score on more number of statistical monitoring devices. It may be noted that in FIG. 1, the statistics monitoring device 118 is associated with the user 118 a; however, those skilled in the art would appreciate that more number of statistics monitoring devices are associated with one or more number of users.

FIG. 2 illustrates a block diagram 200 to calculate the probabilistic score, in accordance with various embodiments of the present disclosure. The probabilistic score is calculated for the determination of the potential for the improvements in the energy consumption inside the built environment 102. It may be noted that to explain the system elements of FIG. 2, references will be made to the system elements of the FIG. 1. Further, the block diagram 200 describes the method for the calculation of the probabilistic score. The block diagram 200 includes the built environment 102, the energy auditing and scoring system 114, the plurality of external APIs 116 and the statistics monitoring device 118.

The built environment 102 includes the plurality of electrical devices 106, one or more physical occupancy devices 202 a, a plurality of sensors 202 b, and one or more physical data sources 202 c. In addition, the built environment 102 includes the one or more data collecting devices 110. The plurality of energy consuming device includes the plurality of electrical devices 106 and the plurality of portable communication devices 108. Further, the plurality of electrical devices 106 offers loads with a pre-defined amount of energy consumption. In addition, the energy consumption at the loads is the electrical energy. The energy consumption of each of the plurality of electrical devices 106 is based on the plurality of factors associated with the load ratings (as described above in the detailed description of the FIG. 1).

Furthermore, the energy auditing and scoring system 114 records the first set of statistical data based on the one or more operating characteristics of each of the plurality of electrical devices 106. In an embodiment of the present disclosure, the first set of statistical data is obtained from interview process conducted by the plurality of users 104. In yet another embodiment of the present disclosure, the first set of statistical data is derived from a control system, timer and computer. The control system drives a schedule to use each of the plurality of electrical devices 106. The schedule is based on an operating schedule.

Moreover, the energy auditing and scoring system 114 records the second set of statistical data in the real time. The second set of statistical data is recorded based on the occupancy pattern and the energy consumption behavior of each of the plurality of users 104. Each of the plurality of users 104 is associated with the plurality of energy consuming devices present inside the built environment 102. Example of the plurality of portable communication devices 108 includes but may not be limited to the mobile phone, the personal digital assistants (hereafter “PDA”) and the laptop. Each portable communication device consumes a pre-defined amount of the electrical energy based on the plurality of factors (as explained above in detailed description of the FIG. 1).

Moreover, the energy usage of the plurality of portable communication devices 108 is derived from the information about the plurality of users 104 associated with the corresponding portable communication device. In addition, the energy usage is derived from the duration of the energy consumption by each of the plurality of portable communication devices 108. For example, an office worker brings the laptop to the office each day and carries the laptop home each night. The office worker enters and leaves the office at different times each day and different days each week. The energy usage of the laptop in the office is derived from the office worker time stamped schedule of entry and leave. The energy auditing and scoring system 100 multiplies the energy usage with the plurality of factors associated with each of the plurality of portable communication devices 108. In addition, the energy auditing and scoring system 100 derives the second set of statistical data from the changes in the energy consumption of the portable communication device. The changes in the energy consumption are based on age and purpose of each of the plurality of portable communication device 108. In an example, the amount of the energy consumption for the laptop with heavy graphics design programs and engineering cad software is different from the laptop used for an email or a word function.

In addition, the plurality of users 104 present inside the built environment 102 varies based on day, month, season, events and time of year. In addition, the real time occupancy of the plurality of users 104 is determined from the one or more physical occupancy devices 202 a (as explained above in the detailed description of the FIG. 1). In addition, the energy auditing and scoring system 114 records the real time occupancy in the second set of statistical data.

Furthermore, the energy auditing and scoring system 100 records ambient parameters that affects the duration of the energy consumption and the real time occupancy of the plurality of users 104. The plurality of sensors 202 b provides the ambient parameters. The plurality of sensors 202 b includes a temperature sensor, a humidity sensor, a pressure sensor, a sound sensor, a vibration sensor and the like. In addition, the one or more physical data sources 202 c provide one or more details. The one or more details include interview forms, bills, digital photographs and the like.

Further, the one or more data collecting devices 110 is configured to perform the collection of the first set of statistical data and the second set of statistical data (As explained in the detailed description of the FIG. 1). Furthermore, the one or more collecting devices 110 are associated with the network through the internet connection (As described above in the detailed description of the FIG. 1). The network is configured to transmit the pre-determined size of the first set of statistical data and the second set of statistical data at the pre-defined rate to the energy auditing and scoring system 114.

The collection of the first set of statistical data and the second set of statistical data is performed through a distributed audit process. The distributed audit process enables collection and analysis of the first set of statistical data and the second set of statistical data corresponding each of the plurality of energy consuming devices and each of the plurality of users 104. In addition, the distributed audit process is applicable for data collection across multiple platforms. The distributed audit process when divided up across a distributed digital platform enables cost effective and relatively faster means of collection of the first set of statistical data and the second set of statistical data.

Each of the one or more data collecting devices 110 may be a mobile device. The mobile device is associated with a user that is responsible for collecting a portion of the first set of statistical data and the second set of statistical data. The mobile device is enabled with a camera, a keypad or keyboard, a global positioning system (GPS), text and data entry application and the like. The user assigned to collect the first set of statistical data and the second set of statistical data may use the mobile device to capture images and nameplate information of each of the plurality of energy consuming devices. In addition, the user may use the mobile device to schedule, record, identify, transfer or tag information related to the identity, usage and location of the plurality of energy consuming devices. The user may tag the images or any records with metadata for current and future analysis of the first set of statistical data and the second set of statistical data.

The mobile device may include one or more mobile applications that are configured to search and display the metadata associated with images and records, captured images, text and the like. The mobile device application installed with the one or more mobile applications may enable any user or any optical reader devices to capture key information about each analyzed energy consuming device. In addition, the metadata for captured images may be added later or remotely by any user.

The metadata and captured key information may be analyzed by an analysis engine of the energy auditing and scoring system 114. The metadata is sorted, categorized, prioritized and applied to calculations of probabilistic score and performance parameters of the built environment. The metadata and the captured key information in the form of first set of statistical data is used to display and generate charts, graphs, tables and other forms of data output. The generated results may be viewed and analyzed by any third party user present inside or outside of the built environment 102.

The energy auditing and scoring system 114 may transfer the analyzed data of the analysis engine to a manipulation engine. The manipulation engine enables display of organized results and the first set of statistical data on a presentation platform for recommendation and review of supervisors of the built environment 102.

The energy auditing and scoring system 114 collects the first set of statistical data associated with the plurality of energy consuming devices. The energy auditing and scoring system 114 collects the current energy consumption data associated with each of the plurality of energy consuming devices. In addition, the energy auditing and scoring system 114 collects the past energy consumption data associated with the each of the plurality of energy consuming devices.

The plurality of external APIs 116 includes one or more databases connected to another network for the collection and the storage of the first set of statistical data. The plurality of external APIs 116 includes weather API 206 a and utility history API 206 b. The weather API 206 a stores data and collects data associated with weather conditions surrounding the built environment 102. In addition, the utility history API 206 b stores the past energy consumption data of the built environment 102. In addition, the first set of data is collected based on a first plurality of parameters. The first plurality of parameters includes the current energy consumption data, a physical location and the duration of the energy usage by each of the plurality of energy consuming devices. In addition, the first plurality of parameters includes a seasonal variation in the energy consumption and an off-seasonal variation in the energy consumption. Moreover, the current energy consumption pertains to each of the plurality of energy consuming devices present inside the built environment 102.

Further, the energy auditing and scoring system 114 receives the second set of statistical data associated with each of the plurality of users 104 present inside the built environment 102. The second set of statistical data is received based on the second plurality of parameters. In addition, the reception of the second set of statistical data is done in the real time. Moreover, the second plurality of parameters includes the occupancy behavior of the plurality of users 104 and the energy consuming pattern associated with the corresponding energy consuming device. In addition, the second plurality of parameters include the physical location of the each of the plurality of users 104 and the duration of the energy usage associated with the corresponding energy consuming device.

Furthermore, the energy auditing and scoring system 114 segregates the first set of statistical data. The energy auditing and scoring system 114 creates the one or more fields. The one or more fields pertain to the energy consumption of each of the plurality of energy consuming devices. Moreover, the one or more fields are based on the one or more operating characteristics of the plurality of plurality of energy consuming devices. In addition, the one or more fields are based on the one or more physical characteristics of the plurality of energy consuming devices. Further, the one or more operating characteristics include the operating voltage, the running load amperage, the full load amperage, the wattage, the voltage and the frequency. Furthermore, the one or more operating characteristics include the temperature and the flow rate. In addition, the one or more physical characteristics include the size, the dimension, the packaging and the shape of each of the plurality of energy consuming devices present in the built environment 102.

Moreover, the energy auditing and scoring system 114 compares the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison determines the potential for the improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment 102. The comparison is done is the real time by the energy auditing and scoring system 114.

Further, the energy auditing and scoring system 114 calculates the probabilistic score. The probabilistic score pertains to the energy consumption of each of the plurality of energy consuming device present inside the built environment 102. In an embodiment of the present disclosure, the energy auditing and scoring system 114 calculates the probabilistic score for the determination of the potential for the improvements in the energy consumption. The calculation of the probabilistic score is derived from a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment 102. In addition, the probabilistic score is calculated for providing the rating to each of the plurality of energy consuming devices present in the built environment 102. The calculation of the probabilistic score is performed in the real time.

Further, the energy auditing and scoring system 114 checks for a saving potential associated with each of the plurality of energy consuming devices present inside the built environment 102. Accordingly, the built environment 102 associated with a lower saving potential is considered a low value project. In addition, the built environment 102 is assigned a low probabilistic score. Further, the built environment 102 associated with a higher saving potential is considered a high value project. Accordingly, the built environment is assigned a high probabilistic score.

Furthermore, the saving potential is a combination of the current energy consumption data and the past energy consumption data. The low saving potential and the high saving potential are separated by a threshold condition for saving potential. The threshold condition is determined based on a factor of the cost and the duration of the energy usage for each of the plurality of energy consuming devices. The duration of the energy usage is compared against the threshold condition. Accordingly, the built environment 102 with too low energy usage for each of the plurality of energy consuming devices is not considered for the energy audit.

Moreover, the duration of the energy usage is checked for each type of the energy consuming devices of the plurality of energy consuming devices in the built environment 102. In an example, low duration of the energy usage for the plurality of sensors is ignored in the determination of the probabilistic score. In addition, too low duration of the energy usage for the air conditioner or flow pump is not ignored. In another example, the energy auditing and scoring system derives the duration of the energy usage for the air conditioners. The energy auditing and scoring system 114 factors the energy consumption based on monthly, weekly, daily and hourly energy usage with the power rating of the air conditioners.

In an embodiment of the present disclosure, the probabilistic score is determined based on proximity of the duration of the energy usage to the low duration of the energy usage. The duration of the energy usage closer to the low duration of the energy usage is assigned the lower probabilistic score. In addition, the duration of the energy usage away from the low duration of the energy usage is assigned the high probabilistic score. In another embodiment of the present disclosure, the probabilistic score for the past energy consumption and the probabilistic score for the current energy consumption is compared. The comparison is performed to analyze an improvement in the energy consumption of the built environment 102.

The energy auditing and scoring system 114 provides the improvement in the probabilistic score by an application of a learning algorithm. The learning algorithm records the first set of statistical data and an operating behavior. The operating behavior pertains to the plurality of users 104. The operating behavior is recorded based on the physical location and the type of the built environment 102. In addition, the operating behavior is based on the duration of the energy usage for each of the plurality of portable communication devices 108 associated with the plurality of users 104.

The energy auditing and scoring system 114 stores the first set of statistical data, the second set of statistical data and the probabilistic score. Further, the energy auditing and scoring system 114 updates the first set of statistical data, the second set of statistical data and the probabilistic score. In addition, the energy auditing and scoring system 114 performs a reverse mathematical calculation with appropriate energy consumption per given temperature. Further, the energy auditing and scoring system 114 accurately predicts the energy consumption of built environment 102 based on hourly, daily, weekly, monthly or some other time period.

Further, the energy auditing and scoring system 114 is connected to the statistics monitoring device 118. The statistics monitoring device 118 is associated with the user 118 a. The user 118 a associated with the statistics monitoring device 118 is a data manager. The user 118 a makes adjustments in the first set of statistical data and the second set of statistical data in order to compensate for the potential errors.

Further, it may be noted that in the FIG. 2, the built environment 102 is connected to the energy auditing and scoring system 114; however, those skilled in the art would appreciate that more number of the built environments are connected to the energy auditing and scoring system 114. It may be noted that in FIG. 2, the energy auditing and scoring system 114 is connected to the statistics monitoring device 118 associated with the user 118 a; however, those skilled in the art would appreciate that the energy auditing and scoring system 114 is connected to more numbers of statistical devices associated with more number of users.

FIG. 3 illustrates a block diagram 300 of the energy auditing and scoring system 114, in accordance with various embodiments of the present disclosure. The probabilistic score is calculated for the determination of the potential for the improvements in the energy consumption inside the built environment. It may be noted that to explain the system elements of the FIG. 3, references will be made to the system elements of the FIG. 1 and the FIG. 2.

The block diagram 300 includes the energy auditing and scoring system 114. The energy auditing and scoring system 114 include a collection module 302, a receiving module 304, a segregating module 306, a comparison module 308, a computation engine 310, a storing module 312 and an update engine 314.

Further, the collection module 302 collects the first set of statistical data associated with the plurality of energy consuming devices present in the built environment 102 (as previously stated above in above in detailed description FIG. 2). The collection is done by one or more data collecting devices 110 located in the built environment 102.

Further, the receiving module 304 receives the second set of statistical data associated with each of the plurality of users 104 present inside the built environment 102 (as explained above in the detailed description of the FIG. 2).

Furthermore, the segregation module 306 segregates the first set of statistical data and the second set of statistical data. The segregation module 306 creates the one or more fields (as elaborated in the detailed description of the FIG. 2). Further, the comparison module 308 compares the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The comparison is performed for determination of the potential for improvement in the energy consumption of each of the plurality of energy consuming devices (as stated above in the detailed description of FIG. 2).

Moreover, the computation engine 310 calculates the probabilistic score. The probabilistic score is calculated to determine the potential for the improvements in the energy consumption inside the built environment 102 (as explained above in the detailed description of FIG. 2).

Further, the computation engine 310 performs identification and assignment of the probabilistic score. In an embodiment of the present disclosure, the computation engine 310 performs a relationship (r²) analysis. The relationship analysis is performed for the past energy consumption data with an average daily and monthly consumption with reference to the ambient temperature. The ambient temperature control is no longer dependent on artificial cooling methods. The artificial cooling methods pertain to the use of the air conditioners for reduction in the energy consumption. Moreover, an energy balance is offset by foreign heat introduced within the built environment 102 by each of the plurality of users 104.

In addition, the probabilistic score associated the energy balance is derived from the relationship formula. The relationship formula generates an output that provides a relationship (r²) value. The relationship (r²) value is a statistical fit of the first set of statistical data and the second set of statistical data. Also, the output generated by the relationship formula is discounted by mechanical efficiency of cooling system and the energy efficiency of the built environment 102. In addition, the output generated by the relationship formula is discounted by an envelope value, an insulation value and a geographic orientation. The output with a small relationship between the temperature and the energy demonstrates poor control of the built environment 102. In addition, the output with the small relationship between the temperature and the energy demonstrates heavy influence on the energy consumption.

The computation engine solves a reverse mathematical equation. In addition, the solution of the reverse mathematical equation provides a possibility to identify unexpected and the unusual variations in the energy consumption. Moreover, the learning algorithm improves the probabilistic score (as described in detail in the detailed description of the FIG. 2). In addition, the computation engine 310 enables a prompt delivery of the probabilistic score. Further, the storing module 312 stores the first set of statistical data, the second set of statistical data and the probabilistic score. Moreover, the update engine 314 updates the first set of statistical data, the second set of statistical data and the probabilistic score.

Further, the energy auditing and scoring system 114 is connected to the statistics monitoring device 118. The statistics monitoring device 118 is associated with the user 118 a. The user 118 a associated with the statistics monitoring device 118 is the data manager. The user 118 a makes the adjustments in the first set of statistical data and the second set of statistical data in order to compensate for the potential errors.

In addition, the energy auditing and scoring system 114 utilizes an algorithm to detect word and number characters. The detection of the word and the number characters reduces the first set of statistical data and the second set of statistical data. Moreover, the energy auditing and scoring system 114 analyzes the images of each of the plurality of energy consuming devices. In addition, the energy auditing and scoring system 114 identifies type, model and manufacturer of the corresponding energy consuming device. The manufacturer and the model of the corresponding energy consuming device are derived from the first set of statistical data. In addition, the model and the manufacturer of the corresponding energy consuming device are derived from the comparison of stored images. In addition, the information provides automatic population of the energy consumption of each of the plurality of energy consuming devices.

For example, a 42″ television in Europe is possibly identified to a common statistical range of the wattage consumption for the 42″ television manufacturer. The energy auditing and scoring system 114 identifies a typical range of the wattage for the 42″ television in the Europe. Accordingly, the energy auditing and scoring system 114 generates a plurality of results based on a statistical formula. The statistical formula applies on the first set of statistical data and the second set of statistical data. The plurality of results includes a table and chart of monthly energy consumption of the built environment 102. In addition, the plurality of results includes a statistical correlation between the energy consumption and a table of a total monthly variable energy load. Also, the plurality of results includes the temperature for the data points cooling degree days and heating degree days. Moreover, the plurality of results includes a pie chart for a separation of the energy consumption in the built environment 102 and a table of the energy consumption per month. Further, the plurality of results include the air conditioner loads, a statistical chart depicting a kWh consumption based on load type and a bar graph of expected air conditioner savings. Furthermore, the plurality of results includes the statistical chart of total kWh consumed per room as a function of cooling degree days.

FIG. 4 illustrates a flow chart 400 for calculation of the probabilistic score, in accordance with various embodiments of the present disclosure. It may be noted that to explain the process steps of flowchart 400, references will be made to the system elements of FIG. 1, FIG. 2 and FIG. 3. It may also be noted that the flowchart 400 may have lesser or more number of steps.

The flowchart 400 initiates at step 402. Following step 402, at step 404, the collection module 302 the first set of statistical data associated with the plurality of energy consuming devices present in the built environment 102. At step 406, the receiving module 304 the second set of statistical data associated with each of the plurality of users 104 present inside the built environment 102. At step 408, the comparison module 308 compares the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data. The calculation is performed for the determination of the potential for the improvements in energy consumption of each of the plurality of energy consuming devices. At step 410, the computation engine 310 calculates the probabilistic score for the determination of the potential for the improvements in the energy consumption inside the built environment 102. The calculation is based on the comparison of the current energy consumption of the first set of statistical data with the past energy consumption of the first set of statistical data. In addition, the comparison is based on the second set of statistical data. The flow chart 400 terminates at step 412.

FIG. 5 illustrates a block diagram of a communication device 500, in accordance with various embodiments of the present disclosure. The communication device enables the hosting of the energy auditing and scoring system 114. The communication device includes a control circuitry module 502, a storage module 504, an input/output circuitry module 506, and a communication circuitry module 508. The communication device includes any suitable type of portable electronic device. The communication device includes but may not be limited to a personal e-mail device (e.g., a Blackberry™ made available by Research in Motion of Waterloo, Ontario), a personal data assistant (“PDA”), a cellular telephone. In addition, the communication device includes a smart phone, the laptop, computer and the tablet. In another embodiment of the present disclosure, the communication device can be a desktop computer.

From the perspective of this disclosure, the control circuitry module 502 includes any processing circuitry or processor operative to control the operations and performance of the communication device. For example, the control circuitry module 502 may be used to run operating system applications, firmware applications, media playback applications, media editing applications, or any other application.

In an embodiment of the present disclosure, the control circuitry module 502 drives a display and process inputs received from the user interface. From the perspective of this disclosure, the storage module 504 includes one or more storage mediums. The one or more storage medium includes a hard-drive, solid state drive, flash memory, permanent memory such as ROM, any other suitable type of storage component, or any combination thereof. The storage module 504 may store, for example, media data (e.g., music and video files), application data (e.g., for implementing functions on the communication device).

From the perspective of this disclosure, the I/O circuitry module 506 may be operative to convert (and encode/decode, if necessary) analog signals and other signals into digital data. In an embodiment of the present disclosure, the I/O circuitry module 506 may convert the digital data into any other type of signal and vice-versa. For example, the I/O circuitry module 506 may receive and convert physical contact inputs (e.g., from a multi-touch screen), physical movements (e.g., from a mouse or sensor), analog audio signals (e.g., from a microphone), or any other input. The digital data may be provided to and received from the control circuitry module 502, the storage module 504, or any other component of the communication device.

It may be noted that the I/O circuitry module 506 is illustrated in FIG. 5 as a single component of the communication device; however those skilled in the art would appreciate that several instances of the I/O circuitry module 506 may be included in the communication device.

The communication device may include any suitable interface or component for allowing the user to provide inputs to the I/O circuitry module 506. The communication device may include any suitable input mechanism. Examples of the input mechanism include but may not be limited to a button, keypad, dial, a click wheel, and a touch screen. In an embodiment, the communication device may include a capacitive sensing mechanism, or a multi-touch capacitive sensing mechanism.

In an embodiment of the present disclosure, the communication device may include specialized output circuitry associated with output devices such as, for example, one or more audio outputs. The audio output may include one or more speakers built into the communication device, or an audio component that may be remotely coupled to the communication device.

The one or more speakers can be mono speakers, stereo speakers, or a combination of both. The audio component can be a headset, headphones or ear buds that may be coupled to the communication device with a wire or wirelessly.

In an embodiment, the I/O circuitry module 506 may include display circuitry for providing a display visible to a user. For example, the display circuitry may include a screen (e.g., an LCD screen) that is incorporated in the communication device.

The display circuitry may include a movable display or a projecting system for providing a display of content on a surface remote from the communication device (e.g., a video projector). In an embodiment of the present disclosure, the display circuitry may include a coder/decoder to convert digital media data into the analog signals. For example, the display circuitry may include video Codecs, audio Codecs, or any other suitable type of Codec.

The display circuitry may include display driver circuitry, circuitry for driving display drivers or both. The display circuitry may be operative to display content. The display content can include media playback information, application screens for applications implemented on the electronic device, information regarding ongoing communications operations, information regarding incoming communications requests, or device operation screens under the direction of the control circuitry module 502. Alternatively, the display circuitry may be operative to provide instructions to a remote display.

In addition, the communication device includes the communication circuitry module 508. The communication circuitry module 508 may include any suitable communication circuitry operative to connect to a communication network. In addition, the communication circuitry module 508 may include any suitable communication circuitry to transmit communications (e.g., voice or data) from the communication device to other devices. The other devices exist within the communications network. The communications circuitry 508 may be operative to interface with the communication network through any suitable communication protocol. Examples of the communication protocol include but may not be limited to Wi-Fi, Bluetooth®, radio frequency systems, infrared, LTE, GSM, GSM plus EDGE, CDMA, and quadband.

In an embodiment, the communications circuitry module 508 may be operative to create a communications network using any suitable communications protocol. For example, the communication circuitry module 508 may create a short-range communication network using a short-range communications protocol to connect to other devices. For example, the communication circuitry module 508 may be operative to create a local communication network using the Bluetooth®, protocol to couple the communication device with a Bluetooth®, headset.

It may be noted that the computing device is shown to have only one communication operation; however, those skilled in the art would appreciate that the communication device may include one more instances of the communication circuitry module 508 for simultaneously performing several communication operations using different communication networks. For example, the communication device may include a first instance of the communication circuitry module 508 for communicating over a cellular network, and a second instance of the communication circuitry module 508 for communicating over Wi-Fi or using Bluetooth®.

In an embodiment of the present disclosure, the same instance of the communications circuitry module 508 may be operative to provide for communications over several communication networks. In another embodiment of the present disclosure, the communication device may be coupled to a host device for data transfers and sync of the communication device. In addition, the communication device may be coupled to software or firmware updates to provide performance information to a remote source (e.g., to providing riding characteristics to a remote server) or performing any other suitable operation that may require the communication device to be coupled to the host device. Several computing devices may be coupled to a single host device using the host device as a server. Alternatively or additionally, the communication device may be coupled to the several host devices (e.g., for each of the plurality of the host devices to serve as a backup for data stored in the communication device).

Moreover, the present disclosure provides numerous advantages over the prior art. The purpose of the energy auditing and scoring system is to solve many problems to ease finance and cost of the operations. In addition, the energy auditing and scoring system eases the energy improvements as a financial or cash based stream, security, investment or other finance mechanism. The energy auditing and scoring system achieves smaller investments for an improved behavior. The improvements identify and implement a high degree of accuracy at a low cost. Accordingly, the built environment, the plurality of users, banks, and companies involved in selecting, implementing, operating and maintaining the improvements can realize intended gains. The realization of the gains is achieved with a low degree of risk at low cost.

Further, the energy auditing and scoring system addresses high costs associated with traditional energy audits. The energy auditing and scoring system accurately provides resulted time of the energy usage based on the energy consumption. In addition, the energy usage is based on the heating and the cooling of the plurality of the energy consuming devices. The plurality of energy consuming devices with a lengthy monitoring period dramatically reduces the associated cost to collect the first set of statistical data and the second set of statistical data. Furthermore, the cost reductions for developing the statistical energy model comes from the segregation of on-site and off-site statistical data collected. An important characteristic of the energy auditing and scoring system is the identification of occupants and operators within the built environment.

Accordingly, multiple built environments that are metered and billed on a common energy utility account are grouped together to form an aggregation of the loads. The energy auditing and scoring system is able to identify the built environment to guide the data manager. The data manager is associated with the operations of the plurality of energy consuming devices inside the built environment. In addition, multiple built environment facilities are mapped out for the statistical analysis.

The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present technology.

While several possible embodiments of the invention have been described above and illustrated in some cases, it should be interpreted and understood as to have been presented only by way of illustration and example, but not by limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments. 

What is claimed is:
 1. A method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising: collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time; receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time; comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.
 2. The method as recited in claim 1, wherein the x-y intercept denotes the energy consumption of each of the plurality of the energy consuming devices.
 3. The method as recited in claim 1, wherein the probabilistic score being improved by an application of a learning algorithm, wherein the application of learning algorithm comprises recording of the first set of statistical data pertaining to each of the plurality of energy consuming devices and an operating behavior pertaining to the plurality of users, wherein the operating behavior being recorded based on a type of the built environment, a physical location, duration of energy usage for each of plurality of portable communication devices associated with the plurality of users.
 4. The method as recited in claim 1, further comprising segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.
 5. The method as recited in claim 1, wherein the plurality of sources of the past energy consumption data and weather conditions comprises a plurality of external application programming interfaces and third party databases.
 6. The method as recited in claim 4, wherein the plurality of energy consuming devices comprises a plurality of electrical devices and a plurality of portable communication devices present inside the built environment and wherein the first set of statistical data being collected manually and electronically.
 7. The method as recited in claim 1, wherein the first plurality of parameters comprises the current energy consumption data pertaining to each of the plurality of energy consuming devices, a physical location, a duration of the energy usage by each of the plurality of energy consuming devices, a seasonal variation in the energy consumption and an off-seasonal variation in the energy consumption;
 8. The method as recited in claim 1, wherein the second plurality of parameters comprises an occupancy behavior of the plurality of users, an energy consuming pattern of a corresponding energy consuming device associated with each of the plurality of users, the physical location of the each of the plurality of users and the duration of the energy usage of the corresponding energy consuming device associated with each of the plurality of users present in the built environment.
 9. The method as recited in claim 1, further comprising storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.
 10. The method as recited in claim 1, further comprising updating the first set of statistical data, the second set of statistical data and the probabilistic score.
 11. A computer system comprising: one or more processors; and a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to perform a method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising: collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time; receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time; comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.
 12. The computer system as recited in claim 11, further comprising segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.
 13. The computer system as recited in claim 11, further comprising storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.
 14. The computer system as recited in claim 11, further comprising updating the first set of statistical data, the second set of statistical data and the probabilistic score.
 15. A computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs a method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising: collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time; receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time; comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.
 16. The computer program product as recited in claim 11, wherein the x-y intercept denotes the energy consumption of each of the plurality of the energy consuming devices.
 17. The computer-readable storage medium as recited in claim 16, wherein the probabilistic score being improved by an application of a learning algorithm, wherein the application of learning algorithm comprises recording of the first set of statistical data pertaining to each of the plurality of energy consuming devices and an operating behavior pertaining to the plurality of users, wherein the operating behavior being recorded based on a type of the built environment, a physical location, duration of energy usage for each of plurality of portable communication devices associated with the plurality of users.
 18. The computer-readable storage medium as recited in claim 16, further comprising instructions for segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.
 19. The computer-readable storage medium as recited in claim 16, further comprising instructions for storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.
 20. The computer-readable storage medium as recited in claim 16, further comprising instructions for updating the first set of statistical data, the second set of statistical data and the probabilistic score. 